Technology Access and Preferences for Smartphone App Interventions to Optimize Iron Chelation Therapy Adherence Among Adolescents, Young Adults, and Parents of Children Receiving Chronic Transfusions: Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Iron chelation therapy (ICT) is essential for people with hematological disorders requiring chronic transfusions to minimize the risk of iron overload, yet suboptimal adherence is prevalent. Widespread use of personal technology makes mobile health (mHealth) an attractive platform to promote adherence. Objective: This study aimed to examine access to mobile technology and preferences for an mHealth intervention to improve adherence to ICT. Methods: A cross-sectional survey that included 63 items assessing technology access, mHealth preferences, and demographics was administered through REDCap (Research Electronic Data Capture), a digital research data tool, during packed red blood cell transfusion visits. Parents of children receiving chronic transfusions, as well as adolescents and young adults receiving chronic transfusions, were enrolled between August 2018 and June 2019. Patients had to have a hematologic diagnosis requiring chronic transfusions, be receiving ICT, and be aged 12 years or older to complete the survey. Parents were required to have a child aged 24 months who met these criteria. Results: A total of 60 participants were included (median age 31.5, IQR 20-39 years; n=40, 67% female), with 29 (48%) being parents and 31 (52%) being patients. All parents and patients owned an electronic tablet, a smartphone, or both. The most endorsed mHealth app features among all participants included laboratory test monitoring (55/60, 92%), reminders to take iron chelation medication (50/60, 83%), and education about ICT (49/60, 82%). Parents' most endorsed features included laboratory test monitoring (27/29, 93%) and education about ICT (25/29, 86%). Patients' most endorsed features included laboratory test monitoring (28/31, 90%) and reminders to take iron chelation medication (28/31, 90%). There were no substantial differences between parents and patients in their preferences. Conclusions: Both parents and adolescents and young adults reported a strong interest in multiple mHealth app features. Participants provided valuable insights into optimal strategies and preferred app features for developing a multifunctional technology-based behavioral intervention to promote ICT adherence.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".